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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.fetch_california_housing</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-california-housing">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_california_housing</span></code></a></li>
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  <div class="section" id="sklearn-datasets-fetch-california-housing">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>.fetch_california_housing<a class="headerlink" href="#sklearn-datasets-fetch-california-housing" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.fetch_california_housing">
<code class="sig-prename descclassname">sklearn.datasets.</code><code class="sig-name descname">fetch_california_housing</code><span class="sig-paren">(</span><em class="sig-param">data_home=None</em>, <em class="sig-param">download_if_missing=True</em>, <em class="sig-param">return_X_y=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/datasets/_california_housing.py#L51"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.fetch_california_housing" title="Permalink to this definition">¶</a></dt>
<dd><p>Load the California housing dataset (regression).</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 50%" />
<col style="width: 50%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p>Samples total</p></td>
<td><p>20640</p></td>
</tr>
<tr class="row-even"><td><p>Dimensionality</p></td>
<td><p>8</p></td>
</tr>
<tr class="row-odd"><td><p>Features</p></td>
<td><p>real</p></td>
</tr>
<tr class="row-even"><td><p>Target</p></td>
<td><p>real 0.15 - 5.</p></td>
</tr>
</tbody>
</table>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#california-housing-dataset"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>data_home</strong><span class="classifier">optional, default: None</span></dt><dd><p>Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.</p>
</dd>
<dt><strong>download_if_missing</strong><span class="classifier">optional, default=True</span></dt><dd><p>If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.</p>
</dd>
<dt><strong>return_X_y</strong><span class="classifier">boolean, default=False.</span></dt><dd><p>If True, returns <code class="docutils literal notranslate"><span class="pre">(data.data,</span> <span class="pre">data.target)</span></code> instead of a Bunch
object.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl>
<dt><strong>dataset</strong><span class="classifier">dict-like object with the following attributes:</span></dt><dd></dd>
<dt><strong>dataset.data</strong><span class="classifier">ndarray, shape [20640, 8]</span></dt><dd><p>Each row corresponding to the 8 feature values in order.</p>
</dd>
<dt><strong>dataset.target</strong><span class="classifier">numpy array of shape (20640,)</span></dt><dd><p>Each value corresponds to the average house value in units of 100,000.</p>
</dd>
<dt><strong>dataset.feature_names</strong><span class="classifier">array of length 8</span></dt><dd><p>Array of ordered feature names used in the dataset.</p>
</dd>
<dt><strong>dataset.DESCR</strong><span class="classifier">string</span></dt><dd><p>Description of the California housing dataset.</p>
</dd>
<dt><strong>(data, target)</strong><span class="classifier">tuple if <code class="docutils literal notranslate"><span class="pre">return_X_y</span></code> is True</span></dt><dd><div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>This dataset consists of 20,640 samples and 9 features.</p>
</dd></dl>

<div class="section" id="examples-using-sklearn-datasets-fetch-california-housing">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_california_housing</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-california-housing" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="Partial dependence plots show the dependence between the target function [2]_ and a set of &#x27;tar..."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_partial_dependence_thumb.png" src="../../_images/sphx_glr_plot_partial_dependence_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/inspection/plot_partial_dependence.html#sphx-glr-auto-examples-inspection-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence Plots</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The sklearn.impute.IterativeImputer class is very flexible - it can be used with a variety of e..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" src="../../_images/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py"><span class="std std-ref">Imputing missing values with variants of IterativeImputer</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Feature 0 (median income in a block) and feature 5 (number of households) of the `California ho..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_all_scaling_thumb.png" src="../../_images/sphx_glr_plot_all_scaling_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py"><span class="std std-ref">Compare the effect of different scalers on data with outliers</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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